Internet of Things (IoT) enables all the general purpose devices/appliances, sensors/actuators to be connected to the Internet, thus allowing a greater control in terms of monitoring, commanding these devices from anywhere and anytime. An example to be given is Building Automation where all the sensors (fire sensors, temperature sensors, etc.), lights, room curtains, door locks, are connected to the Internet and can be controlled remotely/locally using a mobile application or any other easy-to-use device. These devices usually form a network in an ad-hoc mode wherein devices connect to each other to eventually reach a Border Router (BR) through which the devices are connected to the Internet.
Low-Power and Lossy Networks (LLN) is a network of spatially distributed autonomous and resource constrained sensors/actuators to monitor physical or environmental conditions, such as temperature, sound, pressure, etc., and to cooperatively pass their data through the network to a main location. The nodes in such a network are extremely constrained in following aspects: less memory (both RAM, ROM, flash), less processing power, less network bandwidth, less power (battery operated nodes), and the like. The networks when constructed in a mesh formation such that nodes connect to one or more than one intermediate routers are referred to as Ad-hoc Networks. Routing Protocol for LLNs (RPL) is one of the most widely used routing protocol in LLNs. The routing tables/paths are constructed by considering various metrics and constraints such as ETX (Estimated number of transmissions), Latency, and/or Energy of the node. A Network lifetime is defined as the amount of time before a first node in the network fails. Overuse of the node resulting in power drain could be the failure reason.
FIG. 1 illustrates an exemplary scenario to explain the problems of failure of network. As shown in FIG. 1, consider a smart-agriculture scenario where sensors are deployed to measure the moisture level of the soil. The sensors help in analyzing the water requirements for the crops and make optimal use of available water resource. The sensors periodically send information to the remote server where the information is analyzed to decide whether to pump in more water and in which section of farmlands. The sensors are distributed over the farm land and there will usually be only one border router for thousands of such sensors. Thus sensors connect to each other in ad-hoc manner. The sensors will usually be battery operated since it may not be possible to get mains-powered connection in all the areas of interest in the farmland. The ad-hoc network formation decides how the traffic pattern is distributed among the nodes. The more the traffic is handled by the node, the faster its battery is going to be depleted. Thus it becomes necessary that the network formation is such that the overall traffic is optimally distributed.
FIG. 2 illustrates an example of the optimal traffic distribution. As shown in FIG. 2, consider topology I where the network formation shows that Node X acts as a router for Nodes A, B, C, D whereas Node Y in topology I do not receive any traffic from other nodes even though there exists connectivity from other nodes such as C (shown in dotted line). This results in more traffic getting received on Node X whereas Node Y is left idling. This results in relatively more battery consumption on Node X as compared to Node Y, resulting in Node X failing lot before Node Y. In case II, the topology formation is balanced such that node C is connected to Node Y and because of that Node D traffic is also routed through Node Y. Thus Node X and Y share almost equal amount of traffic (considering equal amount of traffic getting generated from each node). Thus the battery consumption in the overall network is evenly balanced at a given level in the tree. This results in optimal lifespan of the network. In order to have a balanced network to achieve the optimal traffic distribution, the prior-art provides various solutions and approaches.
The ETX-based routing metric approach enables is a routing metric wherein a node tries to use a path which results in least number of (re)transmissions. This is one of the most widely adopted routing metric and the default to be used as per the standards. However, the ETX based routing metric results in unbalanced network formation. The reason is that the ETX metric does not give any consideration to network balancing and primarily works for optimizing/reducing the number of transmissions while sending the packet.
The node energy based routing metric approach provides a routing metric wherein a node which has the most battery is preferred as the next hop. This is a reactive approach wherein only once the battery level drops below certain threshold will it be considered to be not used. Meanwhile if the network formation dynamics change then this routing metric will not be able ensure balanced battery consumption. However, Node Energy works by forming a network topology which considers node energy as the routing metric. This is a reactive approach for network balancing. The reason for calling it reactive is because the network topology formation changes once the node energy depletes to a certain threshold. The problem is that during this time the network nodes might have moved and may cause suboptimal balancing of energy in the network.
In view of issues available in prior-art literature there is a dire need to provide a technique to address a network balancing issue under the non-uniform node distribution as current routing protocols does not have any metrics/constraints to deal proactively with network imbalance. Further, it is also required to have an optimal balancing of the network such a way that all the nodes at the same level shares near-about equal traffic. It is desired to reduce the impact of node addition/removal so as to maintain the optimal balancing of the network. Furthermore, is also desired to reduce complexities in the logics to control the overheads be handling uneven traffic and still achieve the proactive balancing of the network.